Computational Tumor Infiltration Phenotypes Enable the Spatial and Genomic Analysis of Immune Infiltration in Colorectal Cancer

Abstract

Cancer immunotherapy has led to significant therapeutic progress in the treatment of metastatic and formerly untreatable tumors. However, drug response rates are variable and often only a subgroup of patients will show durable response to a treatment. Biomarkers that help to select those patients that will benefit the most from immunotherapy are thus of crucial importance. Here, we aim to identify such biomarkers by investigating the tumor microenvironment, i.e., the interplay between different cell types like immune cells, stromal cells and malignant cells within the tumor and developed a computational method that determines spatial tumor infiltration phenotypes. Our method is based on spatial point pattern analysis of immunohistochemically stained colorectal cancer tumor tissue and accounts for the intra-tumor heterogeneity of immune infiltration. We show that, compared to base-line models, tumor infiltration phenotypes provide significant additional support for the prediction of established biomarkers in a colorectal cancer patient cohort (n = 80). Integration of tumor infiltration phenotypes with genetic and genomic data from the same patients furthermore revealed significant associations between spatial infiltration patterns and common mutations in colorectal cancer and gene expression signatures. Based on these associations, we computed novel gene signatures that allow one to predict spatial tumor infiltration patterns from gene expression data only and validated this approach in a separate dataset from the Cancer Genome Atlas.

Spatial heterogeneity of cancer associated protein expression in immunohistochemically stained images as an improved prognostic biomarker

“…In our study we only varied parameter (2) the membrane OD threshold to compute a set of spatial scores corresponding to different resolutions of spatial dependency, and we used fixed values for the two other parameters. Finding an appropriate grid size for spatial analysis tasks in digital pathology is non-trivial and there exists no general solution ( 6 , 7 , 20 ). However, in our study we systematically investigated the influence of the tile size on the overall result, and we showed that our changes of 50% of the tile size had no relevant effect on the results ( Supplementary Figure S5 ).…”

“…To aggregate the cellular expression values into one score per sample, most often simple averaging is performed which does not consider the spatial heterogeneity of the marker expression (5). However, it has been shown that relevant prognostic information can be derived by analyzing the spatial composition of the tumor microenvironment (6)(7)(8), as well as more generally the spatial heterogeneity of protein expression. For example, intra-tumoral heterogeneity has been found to be associated with more aggressive tumors and unfavorable outcomes in many cancer indications (9).…”